---
library_name: transformers
license: mit
base_model: intfloat/e5-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: e5-base-v2-sentiment-twitter
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# e5-base-v2-sentiment-twitter

This model is a fine-tuned version of [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6678
- Accuracy: 0.7186
- F1: 0.7186
- Precision: 0.7219
- Recall: 0.7186

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6842        | 0.1754 | 500  | 0.6403          | 0.7195   | 0.7209 | 0.7232    | 0.7195 |
| 0.6395        | 0.3508 | 1000 | 0.6110          | 0.7215   | 0.7254 | 0.7374    | 0.7215 |
| 0.6188        | 0.5261 | 1500 | 0.6028          | 0.733    | 0.7360 | 0.7442    | 0.733  |
| 0.6291        | 0.7015 | 2000 | 0.5912          | 0.738    | 0.7338 | 0.7403    | 0.738  |
| 0.6005        | 0.8769 | 2500 | 0.5705          | 0.752    | 0.7534 | 0.7572    | 0.752  |
| 0.3942        | 1.0523 | 3000 | 0.6278          | 0.747    | 0.7469 | 0.7525    | 0.747  |
| 0.4603        | 1.2276 | 3500 | 0.6185          | 0.75     | 0.7509 | 0.7536    | 0.75   |
| 0.4579        | 1.4030 | 4000 | 0.6348          | 0.751    | 0.7491 | 0.7526    | 0.751  |
| 0.4264        | 1.5784 | 4500 | 0.6129          | 0.757    | 0.7573 | 0.7579    | 0.757  |
| 0.4196        | 1.7538 | 5000 | 0.6196          | 0.7585   | 0.7582 | 0.7582    | 0.7585 |
| 0.4193        | 1.9291 | 5500 | 0.6159          | 0.7625   | 0.7611 | 0.7615    | 0.7625 |


### Framework versions

- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4